Can dynamic contrast-enhanced MRI evaluate VEGF expression in brain glioma? An MRI-guided stereotactic biopsy study

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Differentiate Between Atypical Cartilaginous Tumors and High-Grade Chondrosarcoma: Correlation With Histological Vessel Characteristics

OBJECTIVE

To differentiate between atypical cartilaginous tumors and high-grade chondrosarcoma of the major long bones using intravoxel incoherent motion (IVIM) and Dynamic Contrast-Enhanced magnetic resonance imaging (DCE-MRI), and explore the correlation of quantitative parameters with hypoxia inducible factor-1α (HIF-1α), vascular endothelial growth factor (VEGF) and microvessel density (MVD).

METHOD

Between September 2016 and March 2022, 35 patients (17 atypical cartilaginous tumors, 18 high-grade chondrosarcoma) underwent MRI examination and pathological confirmation at our hospital. First, IVIM-derived parameters ( D , D* , and f ), and DCE-MRI parameters ( Ktrans , Kep , and V e ) were measured, and intraclass correlation efficient (ICC) and Mann-Whitney U test were performed. Second, receiver-operating characteristic curve analysis was performed to evaluate the diagnostic performance. Finally, Spearman's correlation analysis was performed between the quantitative parameters of IVIM-DWI and DCE-MRI and the immunohistochemical factors HIF-1α, VEGF, and MVD in chondrosarcoma tissue.

RESULTS

D in atypical cartilaginous tumors was significantly higher than that in high-grade chondrosarcoma ( P = 0.003), whereas D* , Ktrans , and K ep in atypical cartilaginous tumors were significantly lower than those in high-grade chondrosarcoma (all P < 0.001). Ktrans demonstrated the highest area under the curve (AUC) of 0.979. The D* , Ktrans , and K ep were positively correlated with HIF-1α, VEGF, and MVD (all P < 0.001), whereas D had no correlation with HIF-1α, VEGF, and MVD ( P = 0.113, 0.077, 0.058, respectively).

CONCLUSION

The IVIM-DWI quantitative parameters ( D , D* ) and DCE-MRI quantitative parameters ( Ktrans , Kep ) are helpful to differentiate between atypical cartilaginous tumors and high-grade chondrosarcoma and could be imaging biomarkers to reflect the expressions of HIF-1α, VEGF, and angiogenesis of chondrosarcoma.

Radiogenomic Analysis of Vascular Endothelial Growth Factor in Patients With Glioblastoma

OBJECTIVES

This article aims to predict the presence of vascular endothelial growth factor (VEGF) expression and to predict the expression level of VEGF by machine learning based on preoperative magnetic resonance imaging (MRI) of glioblastoma (GBM).

METHODS

We analyzed the axial T2-weighted images (T2WI) and T1-weighted contrast-enhancement images of preoperative MRI in 217 patients with pathologically diagnosed GBM. Patients were divided into negative and positive VEGF groups, with the latter group further subdivided into low and high expression. The machine learning models were established with the maximum relevance and minimum redundancy algorithm and the extreme gradient boosting classifier. The area under the receiver operating curve (AUC) and accuracy were calculated for the training and validation sets.

RESULTS

Positive VEGF in GBM was 63.1% (137/217), with a high expression ratio of 53.3% (73/137). To predict the positive and negative VEGF expression, 7 radiomic features were selected, with 3 features from T1CE and 4 from T2WI. The accuracy and AUC were 0.83 and 0.81, respectively, in the training set and were 0.73 and 0.74, respectively, in the validation set. To predict high and low levels, 7 radiomic features were selected, with 2 from T1CE, 1 from T2WI, and 4 from the data combinations of T1CE and T2WI. The accuracy and AUC were 0.88 and 0.88, respectively, in the training set and were 0.72 and 0.72, respectively, in the validation set.

CONCLUSION

The VEGF expression status in GBM can be predicted using a machine learning model. Radiomic features resulting from data combinations of different MRI sequences could be helpful.

Radiosurgery for Brain Metastases: Challenges in Imaging Interpretation after Treatment

Stereotactic radiosurgery (SRS) has transformed the management of brain metastases by achieving local tumor control, reducing toxicity, and minimizing the need for whole-brain radiation therapy (WBRT). This review specifically investigates radiation-induced changes in patients treated for metastasis, highlighting the crucial role of magnetic resonance imaging (MRI) in the evaluation of treatment response, both at very early and late stages. The primary objective of the review is to evaluate the most effective imaging techniques for assessing radiation-induced changes and distinguishing them from tumor growth. The limitations of conventional imaging methods, which rely on size measurements, dimensional criteria, and contrast enhancement patterns, are critically evaluated. In addition, it has been investigated the potential of advanced imaging modalities to offer a more precise and comprehensive evaluation of treatment response. Finally, an overview of the relevant literature concerning the interpretation of brain changes in patients undergoing immunotherapies is provided.

Dynamic contrast-enhanced MRI to characterize angiogenesis in primary epithelial ovarian cancer: An exploratory study

PURPOSE

Angiogenesis is essential for tumor growth. Currently, there are no established imaging biomarkers to show angiogenesis in tumor tissue. The aim of this prospective study was to evaluate whether semiquantitative and pharmacokinetic DCE-MRI perfusion parameters could be used to assess angiogenesis in epithelial ovarian cancer (EOC).

METHOD

We enrolled 38 patients with primary EOC treated in 2011-2014. DCE-MRI was performed with a 3.0 T imaging system before the surgical treatment. Two different sizes of ROI were used to evaluate semiquantitative and pharmacokinetic DCE perfusion parameters: a large ROI (L-ROI) covering the whole primary lesion on one plane and a small ROI (S-ROI) covering a small solid, highly enhancing focus. Tissue samples from tumors were collected during the surgery. Immunohistochemistry was used to measure the expression of vascular endothelial growth factor (VEGF), its receptors (VEGFRs) and to analyse microvascular density (MVD) and the number of microvessels.

RESULTS

VEGF expression correlated inversely with K^trans (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.390, (p = 0.010)), V_e (L-ROI, r = -0.395 (p = 0.009), S-ROI, r = -0.412 (p = 0.006)) and V_p (L-ROI, r = -0.388 (p = 0.011), S-ROI, r = -0.339 (p = 0.028)) values in EOC. Higher VEGFR-2 correlated with lower DCE parameters K^trans (L-ROI, r = -0.311 (p = 0.040), S-ROI, r = -0.337 (p = 0.025)) and V_e (L-ROI, r = -0.305 (p = 0.044), S-ROI, r = -0.355 (p = 0.018)). We also found that MVD and the number of microvessels correlated positively with AUC, Peak and WashIn values.

CONCLUSIONS

We observed that several DCE-MRI parameters correlated with VEGF and VEGFR-2 expression and MVD. Thus, both semiquantitative and pharmacokinetic perfusion parameters of DCE-MRI represent promising tools for the assessment of angiogenesis in EOC.

The Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the Differentiation of Pseudoprogression and Recurrence of Intracranial Gliomas

Objective

The objective of this study was to determine the value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in assessing postoperative changes in intracranial gliomas.

Method

A total of fifty-one patients who had new enhanced lesions after surgical resection followed by standard radiotherapy and chemotherapy were collected retrospectively from October 2014 to June 2021. The patients were divided into a pseudoprogression group (15 cases) and a recurrence group (36 cases) according to the pathological results of the second operation or a follow-up of more than six months. The follow-up data of all patients were complete, and DCE-MRI was performed. The images were processed to obtain the quantitative parameters K^trans, Ve, and Kep and the semiquantitative parameter iAUC, which were analysed with relevant statistical software.

Results

First, the difference in K^trans and iAUC values between the two groups was statistically significant (P < 0.05), and the difference in Ve and Kep values was not statistically significant (P > 0.05). Second, by comparing the area under the curve, threshold, sensitivity and specificity of K^trans, and iAUC, it was found that the iAUC threshold value was slightly higher than that of K^trans, and the specificity of K^trans was equal to that of iAUC, while the area under the curve and sensitivity of K^trans were higher than those of iAUC. Third, K^trans and iAUC had high accuracy in diagnosing recurrence and pseudoprogression, and K^trans had higher accuracy than iAUC.

Conclusion

In this study, DCE-MRI has a certain diagnostic value in the early differentiation of recurrence and pseudoprogression, offering a new method for the diagnosis and assessment of gliomas after surgery.

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Indicators of correct targeting in stereotactic biopsy of intracranial lesions

Background:

Confirmation of whether a stereotactic biopsy was performed in the correct site is usually dependent on the frozen section or on novel tumor-specific markers that are not widely available. Immediate postoperative computed tomography (CT) or magnetic resonance (MR) is routinely performed in our service after biopsy. In this retrospective study, we have carefully analyzed these images in an attempt to determine the presence of markers that indicate appropriate targeting.

Methods:

Medical records and neuroimages of patients who underwent stereotactic biopsy of intracranial lesions were reviewed. The following variables were assessed: age, sex, anatomopathology, lesion site, complications, diagnostic accuracy, and the presence of image markers.

Results:

Twenty-nine patients were included in this case series. About 96.6% of the biopsies were accurate according to the permanent section. Of the 86.2% of patients with intralesional pneumocephalus on the postoperative images, 51.7% additionally presented petechial hemorrhage. In 13.8% of the cases, no image markers were identified.

Conclusion:

This is the first report of intralesional pneumocephalus and petechial hemorrhage as indicators of appropriate targeting in stereotactic biopsy. In the majority of the cases, an immediate postoperative head CT, which is widely available, can estimate how adequate the targeting is. To use intralesional pneumocephalus/ petechial hemorrhages as not only postoperative but also as intraoperative markers of appropriate targeting, it is advised that the surgical wound should be temporarily closed and dressed after the biopsy so that the patient can undergo a CT/MR scan and be checked for the presence of theses markers before removing the stereotactic frame.

Preliminary Study on the Therapeutic Effect of Doxorubicin-Loaded Targeting Nanoparticles on Glioma

Doxorubicin (DOX) is an anthracycline anticancer drug, which is often associated with drug resistance and cytotoxicity. More unfortunately, the biological barrier in the human environment can weaken the efficacy of DOX, such as the blood-brain barrier (BBB). This work attempts to make efforts to solve this problem. We used polyethylene glycol distearoylphosphatidylethanolamine (PEG-DSPE) as a nanocarrier and DOX as a model drug to construct a composite nanodrug (TF-PEG-DSPE/DOX NPs) by coupling transferrin (TF). The results of glioma experiments show that the nanodrug can effectively penetrate BBB to achieve an antitumor effect.

A radiomics model based on DCE-MRI and DWI may improve the prediction of estimating IDH1 mutation and angiogenesis in gliomas

PURPOSE

To investigate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion weighted imaging (DWI) in estimating isocitrate dehydrogenase 1 (IDH1) mutation and angiogenesis in gliomas.

METHOD

One hundred glioma patients with DCE-MRI and DWI were enrolled in this study (training and validation groups with a ratio of 7:3). The IDH1 genotypes and expression of vascular endothelial growth factor (VEGF) in gliomas were assessed by immunohistochemistry. Radiomics features were extracted by an open source software (3DSlicer) and reduced using Least absolute shrinkage and selection operator (Lasso). The support vector machine (SVM) model was developed based on the most useful predictive radiomics features. The conventional model was built by the selected clinical and morphological features. Finally, a combined model including radiomics signature, age and enhancement degree was established. Receiver operator characteristic (ROC) curve was implemented to assess the diagnostic performance of the three models.

RESULTS

For IDH1 mutation, the combined model achieved the highest area under curve (AUC) in comparison with the SVM and conventional models (training group, AUC = 0.967, 0.939 and 0.906; validation group, AUC = 0.909, 0.880 and 0.842). Furthermore, the SVM model showed good diagnostic performance in estimating gliomas VEGF expression (validation group, AUC = 0.919).

CONCLUSIONS

The radiomics model based on DCE-MRI and DWI can have a considerable effect on the evaluation of IDH1 mutation and angiogenesis in gliomas.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

Dynamic contrast-enhanced MRI predicts PTEN protein expression which can function as a prognostic measure of progression-free survival in NPC patients

OBJECTIVES

The objective of our study was to investigate whether a phosphatase and tensin homolog deleted on chromosome 10 (PTEN) expression was associated with dynamic contrast-enhanced MRI (DCE-MRI) parameters and prognosis in nasopharyngeal carcinoma (NPC).

METHODS

Two-hundred-and-forty-five (245) patients with NPC who underwent pretreatment biopsy, expression of PTEN detected by immunohistochemistry of biopsy, and radical intensity-modulated radiation therapy (IMRT) with or without chemotherapy were included. Tumor segmentations were delineated on pretreatment MRI manually. The pharmacokinetic parameters (K^trans, K_ep, V_e, and V_p) derived from dynamic contrast-enhanced MRI (DCE-MRI) using the extended Toft's model within the tumor segmentations were estimated. The following demographics and clinical features were assessed and correlated against each other: gender, age, TNM stage, clinical-stage, Epstein-Barr virus (EBV), pathological type, progression-free survival (PFS), and prognosis status. DCE parameter evaluation and clinical feature comparison between the PTEN positive and negative groups were performed and correlation between PTEN expression with the PFS and prognosis status using Cox regression for survival analysis were assessed.

RESULTS

A significantly lower K^trans and K_ep were found in NPC tumors in PTEN negative patients than in PTEN positive patients. K^trans performed better than K_ep in detecting PTEN expression with the ROC AUC of 0.752. PTEN negative was associated with later TNM stage, later clinical-stage, shorter PFS, and worse prognosis. Moreover, N stage, pathological type, K_ep, and prognostic status can be considered as independent variables in discrimination of PTEN negative expression in NPCs.

CONCLUSIONS

PTEN negative indicated a shorter PFS and worse prognosis than PTEN positive in NPC patients. K^trans and K_ep derived from DCE-MRI, which yielded reliable capability, may be considered as potential imaging markers that are correlated with PTEN expression and could be used to predict PTEN expression noninvasively. Combined radiological and clinical features can improve the performance of the classification of PTEN expression.

Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Analysis of Microvascular Permeability in Peritumor Brain Edema of Fibrous Meningiomas

INTRODUCTION

This study aims to analyze the permeability of intra- and peri-meningiomas regions and compare the microvascular permeability between peritumoral brain edema (PTBE) and non-PTBE using DCE-MRI.

METHODS

This was a retrospective of patients with meningioma who underwent surgery. The patients were grouped as PTBE and non-PTBE. The DCE-MRI quantitative parameters, including volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp), obtained using the extended Tofts-Kety 2-compartment model. Logistic regression analysis was conducted to explore the risk factor of PTBE.

RESULTS

Sixty-three patients, diagnosed as fibrous meningioma, were included in this study. They were 17 males and 46 females, aged from 32 to 88 years old. Kep and Vp were significantly lower in patients with PTBE compared with those without (Kep: 0.1852 ± 0.0369 vs. 0.5087 ± 0.1590, p = 0.010; Vp: 0.0090 ± 0.0020 vs. 0.0521 ± 0.0262, p = 0.007), while there were no differences regarding Ktrans and Ve (both p > 0.05). The multivariable analysis showed that tumor size ≥10 cm3 (OR = 4.457, 95% CI: 1.322-15.031, p = 0.016) and Vp (OR = 0.572, 95%CI: 0.333-0.981, p = 0.044) were independently associated with PTBE in patients with meningiomas.

CONCLUSION

DCE-magnetic resonance imaging·Meningioma·Blood vessel MRI can be used to quantify the microvascular permeability of PTBE in patients with meningioma.

Correlation of dynamic contrast-enhanced magnetic resonance quantitative perfusion parameters with vascular endothelial growth factor expression and microvessel density in rectal cancer

BACKGROUND

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a very important role in assessing tumor nature, blood perfusion, prognosis, and so on. DCE-MRI can quantitatively analyze the blood perfusion state of tumor microcirculation, which can provide valuable hemodynamic information for clinical preoperative evaluation.

AIM

To investigate the correlation of quantitative perfusion parameters of DCE-MRI with vascular endothelial growth factor (VEGF) expression and microvessel density (MVD) in rectal cancer.

METHODS

Sixty-five patients with rectal cancer who were scheduled for surgical resection at our hospital were selected. All patients were examined by DCE-MRI before operation. The quantitative perfusion parameters including forward transport constant (K^trans), reverse transport constant (Kep), volume fraction (Ve), and area under the starting curve (iAUC) of the lesion and normal rectal wall were measured. The MVD and VEGF expression were measured by immunohistochemistry. The correlation of DCE-MRI quantitative perfusion parameters with VEGF expression and MVD in rectal cancer was then analyzed.

RESULTS

K^trans, Kep, Ve, and iAUC in rectal cancer lesions were significantly higher than those in the normal rectal wall (P < 0.05). MVD and VEGF expression in rectal cancer lesions were significantly higher than those in the normal rectal wall (P < 0.05). K^trans, Kep, Ve, and iAUC were positively correlated with MVD (r = 0.76, 0.70, 0.46, and 0.68, respectively, P < 0.05) and VEGF expression (r = 0.72, 0.67, 0.41, and 0.64, respectively, P < 0.05).

CONCLUSION

DCE-MRI quantitative perfusion parameters in rectal cancer can effectively reflect the blood perfusion state of the lesions, and there is a high correlation between K^trans and MVD and VEGF expression, which can provide valuable reference for preoperative evaluation of this malignancy.

DCE-MRI in Glioma, Infiltration Zone and Healthy Brain to Assess Angiogenesis: A Biopsy Study

Purpose

To explore the focal predictability of vascular growth factor expression and neovascularization using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in glioma.

Methods

120 brain biopsies were taken in vital tumor, infiltration zone and normal brain tissue of 30 glioma patients: 17 IDH(isocitrate dehydrogenase)-wildtype glioblastoma (GBM), 1 IDH-wildtype astrocytoma °III (together prognostic group 1), 3 IDH-mutated GBM (group 2), 3 anaplastic astrocytomas IDH-mutated (group 3), 4 anaplastic oligodendrogliomas and 2 low-grade oligodendrogliomas (together prognostic group 4). A mixed linear model evaluated the predictabilities of microvessel density (MVD), vascular area ratio (VAR), mean vessel size (MVS), vascular endothelial growth factor and receptors (VEGF-A, VEGFR‑2) and vascular endothelial-protein tyrosine phosphatase (VE-PTP) expression from Tofts model kinetic and model-free curve parameters.

Results

All kinetic parameters were associated with VEGF‑A (all p < 0.001) expression. K^trans, k_ep and v_e were associated with VAR (p = 0.006, 0.004 and 0.01, respectively) and MVS (p = 0.0001, 0.02 and 0.003, respectively) but not MVD (p = 0.84, 0.74 and 0.73, respectively). Prognostic groups differed in K^trans (p = 0.007) and v_e (p = 0.004) values measured in the infiltration zone. Despite significant differences of VAR, MVS, VEGF‑A, VEGFR‑2, and VE-PTP in vital tumor tissue and the infiltration zone (p = 0.0001 for all), there was no significant difference between kinetic parameters measured in these zones.

Conclusion

The DCE-MRI kinetic parameters show correlations with microvascular parameters in vital tissue and also reveal blood-brain barrier abnormalities in the infiltration zones adequate to differentiate glioma prognostic groups.

Supplementary Information

The online version of this article (10.1007/s00062-021-01015-3) contains supplementary material, which is available to authorized users.

Non-Invasive Estimation of Glioma IDH1 Mutation and VEGF Expression by Histogram Analysis of Dynamic Contrast-Enhanced MRI

Objectives

To investigate whether glioma isocitrate dehydrogenase (IDH) 1 mutation and vascular endothelial growth factor (VEGF) expression can be estimated by histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Methods

Chinese Glioma Genome Atlas (CGGA) database was wined for differential expression of VEGF in gliomas with different IDH genotypes. The VEGF expression and IDH1 genotypes of 56 glioma samples in our hospital were assessed by immunohistochemistry. Preoperative DCE-MRI data of glioma samples were reviewed. Regions of interest (ROIs) covering tumor parenchyma were delineated. Histogram parameters of volume transfer constant (K^trans) and volume of extravascular extracellular space per unit volume of tissue (V_e) derived from DCE-MRI were obtained. Histogram parameters of K^trans, V_e and VEGF expression of IDH1 mutant type (IDH1^mut) gliomas were compared with the IDH1 wildtype (IDH1^wt) gliomas. Receiver operating characteristic (ROC) curve analysis was performed to differentiate IDH1^mut from IDH1^wt gliomas. The correlation coefficients were determined between histogram parameters of K^trans, V_e and VEGF expression in gliomas.

Results

In CGGA database, VEGF expression in IDH^mut gliomas was lower as compared to wildtype counterpart. The immunohistochemistry of glioma samples in our hospital also confirmed the results. Comparisons demonstrated statistically significant differences in histogram parameters of K^transand V_e [mean, standard deviation (SD), 50th, 75th, 90th. and 95th percentile] between IDH1^mutand IDH1^wtgliomas (P < 0.05, respectively). ROC curve analysis revealed that 50th percentile of K^trans (0.019 min⁻¹) and V_e (0.039) provided the perfect combination of sensitivity and specificity in differentiating gliomas with IDH1^mutfrom IDH1^wt. Irrespective of IDH1 mutation, histogram parameters of K^transand V_e were correlated with VEGF expression in gliomas (P < 0.05, respectively).

Conclusions

VEGF expression is significantly lower in IDH1^mut gliomas as compared to the wildtype counterpart, and it is non-invasively predictable with histogram analysis of DCE-MRI.